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Security and privacy are vital aspects of smart transportation management with big data analytics because they assure the security of sensitive information, prevent unwanted access to essential systems, and retain public trust in the safety and dependability of the transportation infrastructure. Protecting data from cyber threats, ensuring secure communication and data transmission, protecting passengers' personal information and addressing privacy concerns related to data collection and usage to maintain transparency and accountability in data handling practices are all obstacles to smart transportation management using big data analytics. This paper proposes a Secure Data Encryption Control based Big Data Framework (SDEC-BDF) to strike a middle ground between data analytics and privacy protection, establishing the way for more private and secure transportation systems that benefit everyone involved. The intention of this approach is to offer strong security while simultaneously safeguarding people's privacy. The method has many potential uses in the Intelligent transportation sector (ITS), including traffic control, passenger security, fleet management, preventative maintenance, and road network design. It ensures privacy and security while facilitating effective data analysis. Furthermore, it protects the public confidence in the security and dependability of the transportation system, protects sensitive passenger data, and stops hackers from breaking into vital systems. The simulation analysis is conducted on the assumption that the system can maximize its security, privacy, and efficiency to create a more trustworthy transportation network.

In the era of digital transformation, supply chain management (SCM) is undergoing a profound shift driven by data science, predictive analytics, and big data. These technologies are not only enhancing operational efficiency but also redefining the strategic design and real-time control of complex supply networks. This paper explores their integration, applications, benefits, and future trends in supply chain management, emphasizing the transition from reactive to intelligent systems. As organizations strive for agility and resilience, data-driven insights enable faster decision-making, forecasting, optimization and proactive risk mitigation. The convergence of IoT, AI, and cloud computing further amplifies the potential for creating highly responsive and adaptive supply chains.